Quantum and digital processor hybrid systems and methods to solve problems

Abstract

Quantum and digital processors are employed together to solve computational problems. The quantum processor may be configured with a problem via a problem Hamiltonian and operated to perform adiabatic quantum computation and / or quantum annealing on the problem Hamiltonian to return a first solution to the problem that is in the neighborhood of the global minimum of the problem Hamiltonian. The digital processor may then be used to refine the first solution to the problem by casting the first solution to the problem as a starting point for a classical optimization algorithm. The classical optimization algorithm may return a second solution to the problem that corresponds to a lower energy state in the neighborhood of the global minimum, such as a ground state of the problem Hamiltonian. The quantum processor may include a superconducting quantum processor implementing superconducting flux qubits.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This is a continuation application of PCT / US2009 / 046791 filed on Jun. 9, 2009, now pending, which claims benefit under 35 U.S.C. 119(e) of U.S. Provisional Patent Application Ser. No. 61 / 060,318, filed Jun. 10, 2008 and entitled “PARAMETER LEARNING SYSTEM FOR SOLVERS”, and U.S. Provisional Patent Application Ser. No. 61 / 095,527, filed Sep. 9, 2008 and entitled “METHODS AND APPARATUS FOR SOLVING COMPUTATIONAL PROBLEMS,” both of which are incorporated herein by reference in their entirety.BACKGROUND OF THE DISCLOSURE[0002]1. Field of the Disclosure[0003]This disclosure generally relates to solvers, and, more particularly, to solvers for computationally complex problems.[0004]2. Description of the Related Art[0005]A Turing machine is a theoretical computing system, described in 1936 by Alan Turing. A Turing machine that can efficiently simulate any other Turing machine is called a Universal Turing Machine (UTM). The Church-Turing thesis stat...

AI Technical Summary

Problems solved by technology

In 1981 Richard P. Feynman proposed that quantum computers could be used to solve certain computational problems more efficiently than a UTM and therefore invalidate the Church-Turing thesis.

However, it is not yet known if there are problems in NP that are not in P.

However, even when employing quantum computation, these algorithms are not guaranteed to find the optimal solution in polynomial time.

Although these heuristic solvers are not guaranteed to find the global optimal solution to these problems, they may find close to the global optimal solution with sufficient run time.

Unfortunately, the ability of such heuristic solvers to find a good solution relatively quickly is often highly dependent upon the particular values chosen for a number of parameters associated with each solver.

Circuit model quantum computers have several serious barriers to practical implementation.

The art is still hampered by an inability to increase the coherence of qubits to acceptable levels for designing and operating practical circuit model quantum computers.

This underlying goal may lead to many complications in the implementation of the algorithm.

Since some anti-crossings may correspond to very small energy gaps, an algorithm focused on achieving an exact solution may require an impractically long evolution schedule.

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